A position debiased search system can avoid bias towards top-ranked search results using a position-trained machine-trained model. Past positions for listings can be input into the model with added noise and low-ranked results to train the model to generate rankings that do not exhibit position bias. A network site can implement the position debiased search system to generate network site results that can generate accurate user results in real time as users browse the network site.
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3. The method of claim 1, wherein the machine learning model is a deep neural network model and the position debiased machine learning scheme is a position debiased deep neural network.
4. The method of claim 1, wherein the arbitrary data is arbitrary in that it is not past position values from the historical search result data.
5. The method of claim 1, wherein the plurality of past results includes a portion of low-positioned results.
6. The method of claim 5, wherein the machine learning model is trained by sampling the low-positioned results.
7. The method of claim 5, wherein the portion of low-positioned results are non-displayed past results.
8. The method of claim 5, wherein the portion of low-positioned results are search results that were not displayed on a first page of search results.
11. The system of claim 9, wherein the machine learning model is a deep neural network model and the position debiased machine learning scheme is a position debiased deep neural network.
12. The system of claim 9, wherein the arbitrary data is arbitrary in that it is not past position values from the historical search result data.
13. The system of claim 9, wherein the plurality of past results includes a portion of low-positioned results.
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October 14, 2019
May 9, 2023
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